Medical image processing system

ABSTRACT

A medical image acquisition unit acquires a medical image obtained by imaging an observation target. A feature amount calculation unit calculates a feature amount of the observation target for each pixel of an image region of the medical image or for each divided region obtained by dividing the image region of the medical image into a specific size. A stage determination unit calculates a distribution index value which is an index value of the spatial distribution of the feature amount of each divided region, and determines the disease stage of the observation target based on the distribution index value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of PCT International Application No.PCT/JP2019/043027 filed on 1 Nov. 2019, which claims priority under 35U.S.C § 119(a) to Japanese Patent Application No. 2018-213697 filed on14 Nov. 2018. The above application is hereby expressly incorporated byreference, in its entirety, into the present application.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a medical image processing system thatdetermines a disease stage indicating a progress stage of a lesion areabased on a medical image.

2. Description of the Related Art

In the current medical field, medical image processing systems that usemedical images, such as an endoscope system comprising a light sourcedevice, an endoscope, and a processor device, are widespread. Further,in recent years, a technology has been used for calculating a featureamount from a medical image to perform diagnosis on a region included inthe medical image, such as a lesion area, based on the calculatedfeature amount.

For example, in JP2006-334115A (corresponding to US2007/135715A1), inorder to detect a medical image having a bleeding site, a feature amount(chromaticity) is calculated for each of regions obtained by dividingthe medical image, and it is determined whether or not each region is ableeding region based on a comparison between the calculated featureamount and a threshold value. Further, in JP2006-166939A, after deletingunnecessary regions from a medical image, a feature amount is calculatedfor each region of the medical image, and the state of a mucosal regionis determined according to the number of regions above a threshold valuewith respect to the total effective region.

SUMMARY OF THE INVENTION

In diagnosis using medical images, in particular, information on aspatial distribution of feature amounts calculated from medical imagesis important in a case of specifying a lesion region or determining adisease stage indicating the progress stage of a lesion area or the likewith high accuracy. For example, in the early stage of Barrett'sesophagus, blood vessels are uniformly distributed throughout, while inthe progress stage, there is a tendency for the distribution of bloodvessels to vary due to regions where erosions occur and blood vesselscannot be seen and regions where blood vessels are dense. On the otherhand, in aforementioned JP2006-334115A and JP2006-166939A, although theregion is determined by using the size of the feature amount, there isno description or suggestion about the region determination or the likeusing the spatial distribution of the feature amount.

An object of the present invention is to provide a medical imageprocessing system capable of determining a disease stage indicating thedegree of progress of a lesion area with high accuracy.

According to an aspect of the present invention, there is provided amedical image processing system comprising: a medical image acquisitionunit that acquires a medical image obtained by imaging an observationtarget; a feature amount calculation unit that calculates a featureamount of the observation target for each of pixels of an image regionof the medical image or for each of divided regions obtained by dividingthe image region of the medical image into a specific size; and a stagedetermination unit that determines a disease stage of the observationtarget by using a spatial distribution of the feature amount.

It is preferable that the stage determination unit includes adistribution index value calculation unit that calculates a distributionindex value which is an index value of the spatial distribution of thefeature amount, and a determination unit that determines the diseasestage based on the distribution index value. It is preferable that thedistribution index value is a variance of the feature amount, and adegree of progress of the disease stage increases as the variance of thefeature amount increases. It is preferable that the distribution indexvalue is an index value related to a spatial distribution of an abnormalregion indicating a pixel or a divided region where the feature amountis outside a specific range. It is preferable that the index valuerelated to the spatial distribution of the abnormal region is at leastone of the number or an area of the abnormal regions or a ratio occupiedby the abnormal region in the image region, and a degree of progress ofthe disease stage increases as the number or the area of the abnormalregions or the ratio of the abnormal region increases.

It is preferable that the stage determination unit includes a firstregion integration unit that integrates abnormal regions indicating apixel or a divided region where the feature amount is outside a specificrange, and the distribution index value is an index value related to aspatial distribution of a specific integrated abnormal region thatsatisfies a specific condition among integrated abnormal regions inwhich the abnormal regions are integrated by the first regionintegration unit. It is preferable that the medical image processingsystem further comprises a second region integration unit thatintegrates adjacent pixels or divided regions in a case where a featureamount of the adjacent pixels or divided regions is within a featureamount range, and the distribution index value is an index value relatedto a spatial distribution of an integrated region in which the adjacentpixels or divided regions are integrated by the second regionintegration unit. It is preferable that the feature amount is at leastone of a blood vessel density, a blood vessel contrast, change in ablood vessel width, or an average value of the blood vessel width.

It is preferable that the feature amount calculation unit calculatesdifferent types of a plurality of calculation feature amounts for eachpixel or for each divided region, and calculates a first calculationvalue obtained by calculation based on the plurality of calculationfeature amounts, as the feature amount. It is preferable that thecalculation feature amount is a blood vessel density of a first layerblood vessel and a blood vessel density of a second layer blood vesseldifferent from the first layer blood vessel, and the first calculationvalue is a ratio of the blood vessel density of the first layer bloodvessel to the blood vessel density of the second layer blood vessel.

It is preferable that the feature amount calculation unit calculatesdifferent types of a plurality of calculation feature amounts for eachpixel or each divided region, and the stage determination unit includesa distribution index value calculation unit that calculates acalculation distribution index value which is an index value of aspatial distribution of each of the calculation feature amounts, andcalculates a second calculation value obtained by calculation based onthe calculation distribution index value, as a distribution index value,and a determination unit that determines the disease stage based on thedistribution index value.

It is preferable that the medical image processing system furthercomprises an effective region setting unit that sets an effective regionin which the disease stage is determinable in the image region, and thefeature amount calculation unit calculates the feature amount for eachdivided region in the effective region. It is preferable that, in a caseof calculating the feature amount for each pixel, the feature amountcalculation unit calculates a feature amount of a specific region, whichis obtained from the specific region including the pixel, as the featureamount for each pixel.

According to the present invention, it is possible to determine adisease stage indicating the degree of progress of a lesion area withhigh accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an image processing system, anendoscope system, and the like.

FIG. 2 is a block diagram showing the endoscope system.

FIG. 3 is a block diagram showing a function of a medical image analysisprocessing unit.

FIG. 4 is an image diagram of a medical image showing an image regionand an effective region.

FIG. 5 is an image diagram of a medical image showing an edge of a capand a specular reflection.

FIG. 6 is an image diagram of a medical image showing an image regionand a divided region.

FIG. 7A is an image diagram of a medical image showing an image regionand a divided region within an effective region.

FIG. 7B is an explanatory diagram showing a pixel PE and a specificregion SR.

FIG. 8 is an image diagram of a medical image showing divided regionsP1, P2, . . . , and Pn.

FIG. 9 is an image diagram of a medical image showing divided regionsPa, Pb, and Pc which are abnormal regions.

FIG. 10 is a block diagram showing a medical image analysis processingunit including a first region integration unit.

FIG. 11 is an image diagram of a medical image showing integratedabnormal regions IR1 and IR2.

FIG. 12 is an image diagram of a medical image showing an integratedabnormal region IR3.

FIG. 13 is a block diagram showing a medical image analysis processingunit including a second region integration unit.

FIG. 14 is an image diagram of a medical image showing an integratedabnormal region IRx.

FIG. 15 is a diagnostic support apparatus including the image processingsystem.

FIG. 16 is a medical service support apparatus including the imageprocessing system.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

As shown in FIG. 1, an image processing system 10 comprises a medicalimage acquisition unit 11, a medical image analysis processing unit 12,a display unit 13, a display control unit 15, an input receiving unit16, an overall control unit 17, and a saving unit 18.

The medical image acquisition unit 11 acquires a medical image includinga subject image, directly from an endoscope system 21 or the like thatis a medical apparatus, or through a management system such as a picturearchiving and communication system (PACS) 22, or other informationsystems. The medical image is a still image or a motion picture (aso-called examination motion picture). In a case where the medical imageis a motion picture, the medical image acquisition unit 11 can acquire aframe image forming a motion picture after examination as a still image.In addition, in a case where the medical image is a motion picture,display of the medical image includes not only displaying a still imageof one representative frame forming the motion picture but alsoreproducing the motion picture once or multiple times. In addition, themedical image acquired by the medical image acquisition unit 11 includesan image automatically captured by a medical apparatus such as theendoscope system 21 regardless of a capturing instruction of a doctor,in addition to an image captured by the doctor using a medical apparatussuch as the endoscope system 21. In the present embodiment, since boththe image processing system 10 and the endoscope system 21 perform imageprocessing using medical images, both the image processing system 10 andthe endoscope system 21 correspond to a medical image processing system.The medical image processing system also includes an ultrasonicdiagnostic apparatus that acquires and displays an image in real time.

In the case of being capable of acquiring a plurality of medical images,the medical image acquisition unit 11 can selectively acquire one or aplurality of medical images among these medical images. In addition, themedical image acquisition unit 11 can acquire a plurality of medicalimages acquired in a plurality of different examinations. For example,it is possible to acquire one or both of a medical image acquired in anexamination performed in the past and a medical image acquired in thelatest examination. That is, the medical image acquisition unit 11 canacquire a medical image optionally.

In the present embodiment, a plurality of medical images each includinga subject image are acquired. More specifically, in a case where amedical image captured in one specific examination is acquired and thereare a plurality of medical images captured in one specific examination,a plurality of medical images are acquired out of a series of medicalimages. In addition, in the present embodiment, the image processingsystem 10 is connected to the endoscope system 21 to acquire a medicalimage from the endoscope system 21. That is, in the present embodiment,the medical image is an endoscopic image.

The display unit 13 is a display for displaying a medical image acquiredby the medical image acquisition unit 11 and an analysis result of themedical image analysis processing unit 12. A monitor or a displayincluded in a device or the like to which the image processing system 10is connected can be shared and used as the display unit 13 of the imageprocessing system 10. The display control unit 15 controls a displayform of the medical image and the analysis result on the display unit13.

The input receiving unit 16 receives inputs from a mouse, a keyboard,and other operation devices connected to the image processing system 10.An operation of each unit of the image processing system 10 can becontrolled using these operation devices.

The overall control unit 17 controls the overall operation of each unitof the image processing system 10. In a case where the input receivingunit 16 receives an operation input using an operation device, theoverall control unit 17 controls each unit of the image processingsystem 10 according to the operation input.

The saving unit 18 saves a still image or the like of a medical image ina storage device (not shown) such as a memory included in the imageprocessing system 10 or a storage device (not shown) included in amedical apparatus such as the endoscope system 21 or the PACS 22.

As shown in FIG. 2, in the present embodiment, the endoscope system 21to which the image processing system 10 is connected includes anendoscope 31 that acquires an image by emitting at least one of light ina white wavelength band or light in a specific wavelength band to imagethe subject, a light source device 32 that emits illumination light tothe inside of the subject through the endoscope 31, a processor device33, and a monitor 34 for displaying a medical image such as anendoscopic image or the like captured using the endoscope 31. The lightin a specific wavelength band that is used as illumination light by theendoscope 31 is, for example, light in a shorter wavelength band thanthe green wavelength band. In particular, the light in a specificwavelength band is light in a blue band or a violet band of the visiblerange.

The processor device 33 comprises a medical image acquisition unit 35, amedical image analysis processing unit 36, and a display control unit37. The medical image acquisition unit 35 acquires a medical imageoutput from the endoscope 31. The medical image analysis processing unit36 performs analysis processing on the medical image acquired by themedical image acquisition unit 35. The processing content of the medicalimage analysis processing unit 36 is the same as the processing contentof the medical image analysis processing unit 12 of the image processingsystem 10. The display control unit 37 displays the medical imageobtained by the medical image analysis processing unit 36 on the monitor34 (display unit). The processor device 33 is connected to the imageprocessing system 10. The medical image acquisition unit 35 is the sameas the medical image acquisition unit 11, the medical image analysisprocessing unit 36 is the same as the medical image analysis processingunit 12, and the display control unit 37 is the same as the displaycontrol unit 15.

The medical image analysis processing unit 36 performs analysisprocessing using the medical image acquired by the medical imageacquisition unit 11. As shown in FIG. 3, the medical image analysisprocessing unit 36 comprises an effective region setting unit 40, afeature amount calculation unit 42, and a stage determination unit 44.

The effective region setting unit 40 sets an effective region in theimage region of the medical image in which the stage determination unit44 can determine the disease stage. Therefore, from the image region ofthe medical image, an indeterminable region in which the disease stagecannot be determined or which interferes with determination of the stageis removed. For example, as shown in FIG. 4, in an image region RP, adark area 46 having a low pixel value is set as an indeterminableregion, and the dark area 46 is removed from the image region RP. Theimage region RP is a display region that displays the entire image ofthe observation target imaged by the endoscope 31. Then, the region inwhich the dark area 46 is removed from the image region RP is set as aneffective region Rx.

In addition to the dark area 46, it is preferable that specific poolssuch as puddles, blood pools, and the like that cover an observationtarget, distortion generated around the image region RP (distortion dueto an objective lens used to image an observation target), image blur,bubbles containing air, and the like are set as indeterminable regions.For example, since a specific pool has a specific color, it is possibleto remove the specific pool by a process of removing the specific color.Further, since the bubbles have a circular shape, it is possible toremove the bubbles by removing the circular structure.

Further, in a case where a cap is attached to the distal end of theinsertion part of the endoscope 31 and an edge 48 of the cap isreflected in the image region of the medical image as shown in FIG. 5,the image of the edge 48 of the cap is also set as an indeterminableregion because it interferes with the determination of the disease stageby the stage determination unit 44. Further, a specular reflection 50caused by the observation target being covered with a transparent mucousmembrane is also set as an indeterminable region because it interfereswith the determination of the disease stage by the stage determinationunit 44. Then, the effective region setting unit 40 sets the region inwhich the edge 48 of the cap or the specular reflection 50 is removedfrom the image region RP as the effective region. An image showing anindeterminable region may be created, the image of the indeterminableregion may be learned by a learning unit (not shown) of the processordevice 33, and the acquired medical image may be input to the learningunit to specify the indeterminable region.

As shown in FIG. 6, the feature amount calculation unit 42 calculates afeature amount of the observation target for each divided region Pobtained by dividing the image region RP of the medical image into aspecific size. For example, in a case where the image size of a medicalimage is 640×480, and 16 divisions are made vertically and horizontally,the specific size of the divided region is about 40×30 (the specificsize of the left and right peripheral parts is smaller than that ofother parts). Since the feature amount is used for determining thedisease stage, as shown in FIG. 7A, it is preferable that the featureamount calculation unit 42 calculates the feature amount of theobservation target for the divided region P in the effective region Rxwhere the disease stage can be determined. In addition, instead ofcalculating the feature amount for each divided region P, the featureamount calculation unit 42 may calculate the feature amount for eachpixel of the image region RP. In this case, for example, as shown inFIG. 7B, it is preferable to set a specific region SR of about 40×30centered on a pixel PE of interest, and calculate the feature amountobtained in the specific region SR as the feature amount of the pixel ofinterest.

It is preferable that the feature amount calculation unit 42 calculates,for example, a blood vessel density of blood vessels, the contrast of ablood vessel, change in blood vessel width, an average value of bloodvessel widths, and the like included in the specific region SR or eachdivided region P as a feature amount.

The blood vessel density is a proportion of blood vessels included inthe specific region SR or the divided region P. The density of bloodvessels is a proportion occupied by the blood vessels in all the pixelsof the divided region P. For blood vessels, it is preferable to use apixel region having a pixel value lower than that of the surroundings asa blood vessel.

The contrast of a blood vessel is a relative contrast of the bloodvessel with respect to the mucous membrane of an observation target. Thecontrast of a blood vessel is calculated by, for example, “YV/YM” or“(YV−YM)/(YV+YM)” using the brightness YV of the blood vessel and thebrightness YM of the mucous membrane.

The change in blood vessel width is a blood vessel index value relatedto a variation in blood vessel width (distance between boundary linesbetween the blood vessel and the mucous membrane) of blood vesselsincluded in the specific region SR or the divided region P, and is alsoreferred to as a degree of caliber disparity. The change in blood vesselwidth is, for example, the rate of change in blood vessel width (alsoreferred to as the degree of expansion). The rate of change in bloodvessel diameter is obtained by “rate of change in blood vessel diameter(%)=minimum diameter/maximum diameter×100” using the thickness of thethinnest part of the blood vessel (minimum diameter) and the thicknessof the thickest part of the blood vessel (maximum diameter). The bloodvessel width is obtained by counting, for example, the number of pixelsin the lateral direction of a blood vessel in the orthogonal directionat specific intervals along the central axis of the extracted bloodvessel.

The average value of blood vessel widths is an average value of bloodvessel widths of blood vessels included in the specific region SR or thedivided region P. Specifically, the average value of blood vessel widthsis calculated by dividing the total blood vessel width of blood vesselsin the divided region P by the number of blood vessels in the dividedregion P.

In addition to the above, the feature amounts calculated by the featureamount calculation unit 42 include the complexity of blood vessel widthchange, the number of blood vessels, the number of crossings betweenblood vessels, the length of a blood vessel, the interval between bloodvessels, the depth of a blood vessel, the height difference of bloodvessels, the inclination of a blood vessel, the area of a blood vessel,the color of a blood vessel, the degree of meandering of a blood vessel,the blood concentration of a blood vessel, the oxygen saturation of ablood vessel, the traveling pattern of a blood vessel, and the bloodflow rate of a blood vessel.

The complexity of blood vessel width change (hereinafter referred to as“complexity of width change”) is a blood vessel index value indicatinghow complicated a change is in a case where the blood vessel width ofthe blood vessel included in the specific region SR or the dividedregion P is changed, and a blood vessel index value calculated bycombining a plurality of blood vessel index values (that is, a rate ofchange in blood vessel diameter, a proportion of a small diameterportion, or a proportion of a large diameter portion) indicating thechange in blood vessel width. The complexity of the thickness change canbe obtained, for example, by the product of the rate of change in theblood vessel diameter and the proportion of the small diameter portion.

The number of blood vessels is the number of blood vessels extracted inthe specific region SR or the divided region P. The number of bloodvessels is calculated using, for example, the number of branch points(number of branches) of the extracted blood vessels, the number ofintersections with other blood vessels (number of crossings), and thelike. The branch angle of a blood vessel is an angle formed by two bloodvessels at a branch point. The distance between branch points is alinear distance between any branch point and a branch point adjacentthereto, or a length along a blood vessel from any branch point to abranch point adjacent thereto.

The number of crossings between blood vessels is the number ofintersections at which blood vessels having different submucosal depthscross each other in the specific region SR or the divided region P. Morespecifically, the number of crossings between blood vessels is thenumber of blood vessels, which are located at relatively shallowsubmucosal positions, crossing blood vessels located at deep positions.

The length of a blood vessel is the number of pixels counted in thelongitudinal direction of the blood vessel extracted from the specificregion SR or the divided region P.

The interval between blood vessels is the number of pixels showing themucous membrane between edges of the blood vessels extracted from thespecific region SR or the divided region P. In a case where the numberof extracted blood vessels is one, the interval between blood vesselshas no value.

The depth of a blood vessel is measured with respect to the mucousmembrane (more specifically, the surface of the mucous membrane). Thedepth of a blood vessel with respect to the mucous membrane can becalculated, for example, based on the color of the blood vessel. In thecase of the special observation image, for example, a superficial bloodvessel near the mucosal surface (shallow submucosal position) isexpressed by a magenta type color, and a middle-deep blood vessel farfrom the mucosal surface (deep submucosal position) is expressed by acyan type color. Therefore, the depth of the blood vessel with respectto the mucous membrane is calculated for each pixel based on the balanceof the signals of respective colors of R, G, and B of the pixelsextracted as a blood vessel.

The height difference of a blood vessel is the magnitude of thedifference in the depth of the blood vessel included in the specificregion SR or the divided region P. For example, the height difference ofone blood vessel of interest is obtained by the difference between thedepth of the deepest part (maximum depth) of the blood vessel and thedepth of the shallowest part (minimum depth) thereof. In a case wherethe depth is constant, the height difference is zero.

The inclination of a blood vessel is the rate of change in the depth ofthe blood vessel included in the specific region SR or the dividedregion P, and is calculated using the length of the blood vessel and thedepth of the blood vessel. That is, the inclination of a blood vessel isobtained by “inclination of blood vessels =depth of blood vessel/lengthof blood vessel”. The blood vessel may be divided into a plurality ofsections, and the inclination of the blood vessel may be calculated ineach section.

The area of a blood vessel is the number of pixels of the blood vesselpixels included in the specific region SR or the divided region P, or avalue proportional to the number of pixels of the pixels extracted as ablood vessel. The area of a blood vessel is calculated within the regionof interest, outside the region of interest, or for the entireendoscopic image.

The color of a blood vessel is each value of RGB of pixels showing theblood vessels included in the specific region SR or the divided regionP. The change in the color of a blood vessel is a difference or ratiobetween the maximum value and the minimum value of each value of the RGBof the pixels showing the blood vessels. For example, the ratio betweenthe maximum value and the minimum value of the B value of a pixelshowing the blood vessel, the ratio between the maximum value and theminimum value of the G value thereof, or the ratio between the maximumvalue and the minimum value of the R value thereof indicates a change inthe color of the blood vessel. Needless to say, conversion intocomplementary colors may be performed to calculate the color of theblood vessel and a change in the color of the blood vessel for eachvalue of cyan, magenta, yellow, green, and the like.

The degree of meandering of a blood vessel is a blood vessel index valueindicating the width of a range in which the blood vessel included inthe specific region SR or the divided region P travels meandering. Thedegree of meandering of blood vessels is, for example, the area (numberof pixels) of the smallest rectangle including the blood vessel forwhich the degree of meandering is to be calculated. Further, the ratioof the length of the blood vessel to the linear distance between thestart point and the end point of the blood vessel may be used as thedegree of meandering of the blood vessel.

The blood concentration of a blood vessel is a blood vessel index valueproportional to the amount of hemoglobin in a blood vessel included inthe specific region SR or the divided region P. Since the ratio (G/R) ofthe G value to the R value of a pixel showing a blood vessel isproportional to the amount of hemoglobin, the blood concentration can becalculated for each pixel by calculating the G/R value.

The oxygen saturation of a blood vessel is the amount of oxygenatedhemoglobin to the total amount of hemoglobin (total amount of oxygenatedhemoglobin and reduced hemoglobin) in a blood vessel included in thespecific region SR or the divided region P. The oxygen saturation can becalculated using an endoscopic image obtained in a case where theobservation target is illuminated with light in a specific wavelengthband (for example, blue light with a wavelength of about 470±10 nm)having a large difference between the light absorption coefficients ofoxygenated hemoglobin and reduced hemoglobin. In a case where blue lightwith a wavelength of about 470±10 nm is used, the B value of the pixelshowing the blood vessel correlates with the oxygen saturation.Therefore, by using a table or the like that associates the B value withthe oxygen saturation, it is possible to calculate the oxygen saturationof each pixel showing the blood vessel.

The proportion of arteries is the proportion of the number of pixels ofarteries to the number of pixels of all blood vessels. Similarly, theproportion of veins is the proportion of the number of pixels of veinsto the number of pixels of all blood vessels. Arteries and veins can bedistinguished by oxygen saturation. For example, assuming that a bloodvessel having an oxygen saturation of 70% or more is an artery and ablood vessel having an oxygen saturation of less than 70% is a vein,extracted blood vessels can be distinguished into arteries and veins.Therefore, it is possible to calculate the proportion of arteries andthe proportion of veins.

The concentration of an administered coloring agent is the concentrationof a coloring agent sprayed on the observation target or theconcentration of a coloring agent injected into the blood vessel byintravenous injection. The concentration of the administered coloringagent is calculated, for example, by the ratio of the pixel value of thecoloring agent color to the pixel value of a pixel other than thecoloring agent color. For example, in a case where a coloring agent forcoloring in blue is administered, B/G, B/R, and the like indicate theconcentration of the coloring agent fixed (or temporarily adhered) tothe observation target. B/G is the ratio of the pixel value of the blueimage of the medical image to the pixel value of the green image of themedical image, and B/R is the ratio of the pixel value of the blue imageof the medical image to the pixel value of the red image of the medicalimage.

The traveling pattern of a blood vessel is a blood vessel index valuerelated to the traveling direction of a blood vessel included in thespecific region SR or the divided region P. The traveling pattern of ablood vessels is, for example, an average angle (traveling direction) ofa blood vessel with respect to an optionally set reference line, avariance (variation in the traveling direction) of an angle formed by ablood vessel with respect to an optionally set reference line, and thelike.

The blood flow rate of a blood vessel is the number of red blood cellspassing through in a blood vessel included in the specific region SR orthe divided region P per unit time. The blood flow rate of a bloodvessel can be obtained by calculating the Doppler shift frequency ofeach pixel showing a blood vessel in the endoscopic image using a signalobtained by an ultrasonic probe, for example, in a case where theultrasonic probe is used together through a forcep channel of theendoscope 31 or the like.

The stage determination unit 44 determines the disease stage of theobservation target by using the spatial distribution of the featureamount calculated by the feature amount calculation unit 42. The stagedetermination unit 44 comprises a distribution index value calculationunit 44 a that calculates a distribution index value which is an indexvalue of the spatial distribution of the feature amount, and adetermination unit 44 b that determines the disease stage based on thedistribution index value (refer to FIG. 3). The spatial distribution ofthe feature amount means the spread of the feature amount in the imageregion RP, and specifically, indicates the difference in the size of thefeature amount between pixels in the image region.

Here, the disease stage is preferably represented by the degree ofprogress of the lesion area. In general, it is considered that thedegree of progress of the disease stage increases as the spatialdistribution of the feature amount varies. For example, in a case wherethe lesion area is Barrett's esophagus, in the early stage, “the bloodvessels are uniformly distributed throughout” and “the blood vesselwidth does not change much”. That is, in the case of the early stage, itis considered that the spatial distribution of the feature amount has alittle variation. On the other hand, in the progress stage, “there is aregion where the blood vessel cannot be seen due to inflammation anderosion”, “the blood vessel width changes depending on the region”, and“the change in blood vessel width becomes large”. That is, in the caseof the progress stage, it is considered that the spatial distribution ofthe feature amount varies.

In the present embodiment, the distribution index value is used todetermine the disease stage, but the disease stage may be determined byother methods. For example, a learning unit (not shown) of the processordevice 33 is made to learn a spatial distribution image of the featureamount and the disease stage determined from the distribution image aslearning data. Machine learning methods such as deep learning are usedfor learning. Then, by inputting the spatial distribution image of thefeature amount calculated by the feature amount calculation unit 42 intothe learning unit, the disease stage is output from the learning unit.

The distribution index value calculation unit 44 a obtains, for example,the variance of the feature amount as the distribution index value.Specifically, in a case where the feature amount is calculated for eachdivided region, as shown in FIG. 8, a total value S is calculated byadding all the feature amounts of divided regions P1, P2, . . . , andPn, and an average feature amount Ave is calculated by dividing thetotal value S by the number N of the divided regions. Next, a squareddeviation V1 (=(feature amount of divided region P1−Ave)²) is calculatedby squaring the feature amount of the divided region P1 minus theaverage feature amount Ave. Similarly, for the feature amounts of thedivided regions P2 to Pn, squared deviations V2 (=(feature amount ofdivided region P2−Ave)²), . . . , and Vn (=(feature amount of dividedregion Pn−Ave)²) are calculated by squaring each of the feature amountsminus the average feature amount Ave. Then, a variance Vx is obtained bysumming all the calculated squared deviations V1, V2, . . . , and Vn.

In a case where the feature amount is calculated for each pixel, thetotal value is calculated by adding all the feature amounts calculatedfor each pixel, and the average feature amount is calculated by dividingthe total value by the number of pixels in the effective region. Next,the squared deviation (=(feature amount of each pixel−average featureamount)²) is calculated by squaring the feature amount of each pixelminus the average feature amount. Then, the variance is obtained bysumming all the squared deviations in the effective region.

The determination unit 44 b determines the disease stage based on thevariance obtained by the distribution index value calculation unit 44 a.In this case, it is considered that the spatial distribution of thefeature amount varies as the variance of the feature amount increases,so that it is determined that the degree of progress of the diseasestage increases.

The distribution index value calculation unit 44 a specifies, forexample, a pixel or a divided region where the feature amount is outsidea specific range as an abnormal region, and calculates an index valuerelated to a spatial distribution of the abnormal region as adistribution index value. In the case of the divided region, forexample, as shown in FIG. 9, as for the abnormal region, among thedivided regions P1, P2, . . . , Pa, Pb, Pc, . . . , and Pn, the dividedregions Pa, Pb, and Pc where the feature amount is outside the specificrange are specified as abnormal regions. As an index value related tothe spatial distribution of the abnormal region, there is the number andarea of the abnormal regions, or the ratio of the area occupied by theabnormal region in the image region RP (area ratio of the abnormalregion). For example, in a case where the abnormal region is dividedregions Pa, Pb, and Pc, the area of each divided region is M, and thenumber of divided regions is N, the number of abnormal regions is “3”,the area of the abnormal regions is “3M”, and the area ratio of theabnormal regions is “3M/NM”. The spatial distribution of the abnormalregion means the spread of the abnormal region in the image region RP,and specifically, indicates the difference between the abnormal regionsof the image region RP. Further, the “spatial distribution of a specificintegrated abnormal region” or the “spatial distribution of anintegrated region” shown below is also defined in the same manner.

The determination unit 44 b determines the disease stage based on theindex value related to the spatial distribution of the abnormal region.For example, as an index value related to the spatial distribution ofthe abnormal region, it is considered that the spatial distribution ofthe feature amount varies as the number or an area of the abnormalregions or the area ratio of the abnormal regions increases. Therefore,it is determined that the degree of progress of the disease stageincreases. In either case of calculating the feature amount for eachpixel or calculating the feature amount for each divided region, it ispossible to determine the degree of progress of the disease stage basedon the number of pixels in the abnormal region where the feature amountis outside the specific range.

Second Embodiment

In a second embodiment, abnormal regions indicating a pixels or adivided region where a feature amount is outside a specific range areintegrated, and an index value related to the spatial distribution of aspecific integrated abnormal region that satisfies a specific conditionamong integrated abnormal regions in which the abnormal regions areintegrated is calculated as a distribution index value. In the secondembodiment, the index value related to the spatial distribution of thespecific integrated abnormal region is, for example, at least one of thenumber or the area of the specific integrated abnormal regions, or theratio occupied by the integrated abnormal region in the image region.

Also in the second embodiment, the distribution index value calculationunit 44 a specifies an abnormal region indicating a pixel or a dividedregion where the feature amount is outside the specific range. Here, theindex value related to the spatial distribution of the abnormal regionis not used as the distribution index value as it is. Then, as shown inFIG. 10, a first region integration unit 44 c provided in the medicalimage analysis processing unit 36 integrates a plurality of adjacentabnormal regions as integrated abnormal regions in a case where theabnormal regions are adjacent to each other. Then, the first regionintegration unit 44 c sets an integrated abnormal region having an areaof a certain value or more or an integrated abnormal region occupying acertain ratio or more in the image region as a specific integratedabnormal region satisfying a specific condition. Then, the distributionindex value calculation unit 44 a calculates at least one of the numberor the area of the specific integrated abnormal regions or the ratiooccupied by the specific integrated abnormal region in the image regionas the distribution index value.

Taking the case where the abnormal region is a divided region as anexample, for example, as shown in FIG. 11, in a case where the abnormalregions Pa, Pb, and Pc are adjacent to each other, and abnormal regionsPd, Pe, and Pf are distributed adjacent to each other at positions awayfrom these abnormal regions Pa, Pb, and Pc, the first region integrationunit 44 c integrates the abnormal regions Pa, Pb, and Pc into anintegrated abnormal region IR1, and integrates the abnormal regions Pd,Pe, and Pf into an integrated abnormal region IR2. Here, assuming thatthe areas of the integrated abnormal regions IR1 and IR2 are below acertain value, the integrated abnormal regions IR1 and IR2 are not setas specific integrated abnormal regions.

On the other hand, as shown in FIG. 12, in a case where the abnormalregions Pa, Pb, Pc, Pd, Pe, and Pf are distributed adjacent to eachother, the first region integration unit 44 c integrates the abnormalregions Pa, Pb, Pc, Pd, Pe, and Pf into an integrated abnormal regionIR3. Then, assuming that the area of the integrated abnormal region IR3is equal to or larger than a certain value, the integrated abnormalregion IR3 is set as a specific integrated abnormal region. At least oneof the number or the area of the integrated abnormal regions IR3 or theratio occupied by the specific integrated abnormal region IR3 in theimage region RP is calculated as the distribution index value.

In a case where the number or the area of the specific integratedabnormal regions or the ratio occupied by the specific integratedabnormal region in the image region is calculated as the distributionindex value, the determination unit 44 b determines the disease stagebased on the area of the specific integrated abnormal regions and thelike. For example, it is considered that the spatial distribution of thefeature amount varies as the area of the specific integrated abnormalregion increases, so that it is determined that the degree of progressof the disease stage increases. In either case of calculating thefeature amount for each pixel or calculating the feature amount for eachdivided region, it is possible to determine the degree of progress ofthe disease stage based on the number of specific integrated abnormalregions and the like.

In the second embodiment, instead of integrating the abnormal regions,in a case where a feature amount of adjacent pixels or divided regionsis within a feature amount range, the adjacent pixels or divided regionsmay be integrated. The integration of the pixels or the divided regionsis performed by a second region integration unit 43 in the medical imageanalysis processing unit 36 shown in FIG. 13. Then, the distributionindex value calculation unit 44 a calculates an index value related tothe spatial distribution of the integrated region in which the adjacentpixels or divided regions are integrated, as the distribution indexvalue. The index value related to the spatial distribution of theintegrated region is, for example, the variance of the integratedregion.

Taking the case of the divided region as an example, for example, asshown in FIG. 14, in a case where a divided region Ps having the featureamount Cs, a divided region Pt having a feature amount Ct, a dividedregion Pu having a feature amount Cu, a divided region Pv having afeature amount Cv, and a divided region Pw having a feature amount Cware distributed adjacent to each other, the feature amounts Cs, Ct, andCu are within a feature amount range, and in a case where the featureamounts Cv and Cw are outside the feature amount range, the dividedregions Ps, Pt, and Pu are integrated to form an integrated region IRx.Then, the variance of the integrated region IRx is calculated as adistribution index value.

In a case where the number or the area of the integrated regions or theratio occupied by the integrated region in the image region iscalculated as the distribution index value, the determination unit 44 bdetermines the disease stage based on the number of the integratedregions and the like. For example, it is considered that the spatialdistribution of the feature amount varies as the number of theintegrated regions increases, so that it is determined that the degreeof progress of the disease stage increases. In either case ofcalculating the feature amount for each pixel or calculating the featureamount for each divided region, it is possible to determine the degreeof progress of the disease stage based on the number of integratedregions and the like.

In the first and second embodiments, as the feature amount, the bloodvessel density, the blood vessel contrast, the change in blood vesselwidth, the average value of the blood vessel width, and the like arecalculated, but a combination thereof may be calculated as the featureamount. For example, the feature amount calculation unit 42 maycalculate different types of a plurality of calculation feature amountsfor each pixel or for each divided region to calculate a firstcalculation value obtained by calculation based on the plurality ofcalculation feature amounts, as the feature amount. In a case ofcalculating a plurality of calculation feature amounts for each pixel,the plurality of calculation feature amounts calculated in the specificregion SR is preferably used as the feature amount for each pixel, as inthe above embodiment.

For example, in a case where two calculation feature amounts of a bloodvessel density of a most superficial blood vessel (first layer bloodvessel) and a blood vessel density of a superficial blood vessel (secondlayer blood vessel) located in a region deeper than the most superficialblood vessel are calculated in the specific region SR or each dividedregion, a blood vessel density ratio (first calculation value) obtainedby dividing the blood vessel density of the most superficial bloodvessel by the blood vessel density of the superficial blood vessel maybe calculated as the feature amount. In this case, for a blood vesseldensity of the most superficial blood vessel, it is preferable tocalculate a blood vessel density of the most superficial blood vesselincluded in each divided region by using a 410 nm-medical image obtainedbased on illumination light including narrow-band light of 410 nm. Inaddition, for a blood vessel density of the superficial blood vessel, itis preferable to calculate a blood vessel density of the superficialblood vessel included in each divided region by using a 450 nm-medicalimage obtained based on illumination light including narrow-band lightof 450 nm.

Further, the feature amount calculation unit 42 calculates differenttypes of a plurality of calculation feature amounts for each specificregion SR or for each divided region, and then the distribution indexvalue calculation unit 44 a may calculate a calculation distributionindex value which is an index value of a spatial distribution of each ofthe calculation feature amounts, and calculate a second calculationvalue obtained by calculation based on the calculated calculationdistribution index value, as a distribution index value.

Further, as described above, in the case where two calculation featureamounts of a blood vessel density of a most superficial blood vessel anda blood vessel density of a superficial blood vessel located in a regiondeeper than the most superficial blood vessel are calculated in thespecific region SR or each divided region, a distribution index value ofthe most superficial blood vessel, which is an index value of thespatial distribution of the blood vessel density of the most superficialblood vessel, is calculated, and a distribution index value of thesuperficial blood vessel, which is an index value of the spatialdistribution of the blood vessel density of the superficial bloodvessel, is calculated. Then, a distribution index value ratio (secondcalculation value) obtained by dividing the distribution index value ofthe most superficial blood vessel by the distribution index value of thesuperficial blood vessel may be calculated as the distribution indexvalue.

In addition, the image processing system 10, the endoscope system 21,and various devices or systems including the image processing system 10can be used with the following various modifications.

As the medical image, it is possible to use a normal light imageobtained by emitting light in a white band or light in a plurality ofwavelength bands as light in the white band.

In a case where an image obtained by emitting light in a specificwavelength band is used as the medical image, a band narrower than thewhite wavelength band can be used as the specific wavelength band.

The specific wavelength band is, for example, a blue band or a greenband of a visible range.

In a case where the specific wavelength band is the blue band or thegreen band of a visible range, it is preferable that the specificwavelength band includes a wavelength band of 390 nm to 450 nm or awavelength band of 530 nm to 550 nm and that light in the specificwavelength band has a peak wavelength within the wavelength band of 390nm to 450 nm or the wavelength band of 530 nm to 550 nm.

The specific wavelength band is, for example, a red band of a visiblerange.

In a case where the specific wavelength band is the red band of avisible range, it is preferable that the specific wavelength bandincludes a wavelength band of 585 nm to 615 nm or a wavelength band of610 nm to 730 nm and that light in the specific wavelength band has apeak wavelength within the wavelength band of 585 nm to 615 nm or thewavelength band of 610 nm to 730 nm.

The specific wavelength band can include, for example, a wavelength bandin which light absorption coefficients of oxygenated hemoglobin andreduced hemoglobin are different, and light in the specific wavelengthband can have a peak wavelength in the wavelength band in which lightabsorption coefficients of oxygenated hemoglobin and reduced hemoglobinare different.

In a case where the specific wavelength band includes a wavelength bandin which the light absorption coefficients of oxygenated hemoglobin andreduced hemoglobin are different and light in the specific wavelengthband has a peak wavelength in the wavelength band in which the lightabsorption coefficients of oxygenated hemoglobin and reduced hemoglobinare different, it is preferable that the specific wavelength bandincludes a wavelength band of 400±10 nm, 440±10 nm, 470±10 nm, or 600 nmto 750 nm and that light in the specific wavelength band has a peakwavelength within the wavelength band of 400±10 nm, 440±10 nm, 470±10nm, or 600 nm to 750 nm.

In a case where the medical image is an in-vivo image of the livingbody, the in-vivo image can have information on fluorescence emittedfrom the fluorescent material in the living body.

In addition, as the fluorescence, fluorescence obtained by emittingexcitation light having a peak wavelength of 390 nm to 470 nm to theinside of the living body can be used.

In a case where the medical image is an in-vivo image of the livingbody, the wavelength band of infrared light can be used as the specificwavelength band described above.

In a case where the medical image is an in-vivo image of the living bodyand the wavelength band of infrared light is used as the specificwavelength band described above, it is preferable that the specificwavelength band includes a wavelength band of 790 nm to 820 nm or 905 nmto 970 nm and that light in the specific wavelength band has a peakwavelength within the wavelength band of 790 nm to 820 nm or 905 nm to970 nm.

The medical image acquisition unit 11 can have a special light imageacquisition unit that acquires a special light image having a signal ina specific wavelength band on the basis of a normal light image obtainedby emitting light in a white band or light in a plurality of wavelengthbands as light in the white band. In this case, the special light imagecan be used as the medical image.

The signal in a specific wavelength band can be obtained by calculationbased on the color information of RGB or CMY included in the normallight image.

It is possible to comprise a feature amount image generation unit thatgenerates a feature amount image by calculation based on at least one ofthe normal light image obtained by emitting light in a white band orlight in a plurality of wavelength bands as light in the white band orthe special light image obtained by emitting light in a specificwavelength band. In this case, the feature amount image can be used asthe medical image.

In the endoscope system 21, a capsule endoscope can be used as theendoscope 31. In this case, the light source device 32 and a part of theprocessor device 33 can be mounted in the capsule endoscope.

In the embodiment, the present invention is applied to the endoscopesystem that performs processing on the endoscopic image as one of themedical images. However, the present invention can also be applied to amedical image processing system that processes medical images other thanthe endoscopic image. The present invention can also be applied to adiagnostic support apparatus for performing diagnosis support for a userusing the medical image. The present invention can also be applied to amedical service support apparatus for supporting the medical service,such as a diagnostic report, using the medical image.

For example, as shown in FIG. 15, a diagnostic support apparatus 610 isused in combination with a modality such as an image processing system10 and a PACS 22. As shown in FIG. 16, a medical service supportapparatus 630 is connected to various examination apparatuses such as afirst medical image processing system 621, a second medical imageprocessing system 622, . . . , and an N-th medical image processingsystem 623 through a certain network 626. The medical service supportapparatus 630 receives medical images from the first to N-th medicalimage processing systems 621, 622, . . . , and 623, and supports themedical service on the basis of the received medical images.

In the above embodiment and modification examples, hardware structuresof processing units for executing various kinds of processing, such asthe medical image acquisition unit 11, the medical image analysisprocessing unit 12, each unit forming the medical image analysisprocessing unit 12, the display control unit 15, the input receivingunit 16, the overall control unit 17, the medical image acquisition unit35, the medical image analysis processing unit 36, the display controlunit 37, the effective region setting unit 40, the feature amountcalculation unit 42, the second region integration unit 43, the stagedetermination unit 44, the distribution index value calculation unit 44a, the determination unit 44 b, and the first region integration unit 44c, are various processors shown below. The various processors include acentral processing unit (CPU) that is a general-purpose processor thatfunctions as various processing units by executing software (program), aprogrammable logic device (PLD) that is a processor whose circuitconfiguration can be changed after manufacture, such as fieldprogrammable gate array (FPGA), a dedicated electrical circuit that is aprocessor having a circuit configuration designed exclusively forexecuting various types of processing, a graphical processing unit(GPU), and the like.

One processing unit may be configured by one of various processors, ormay be configured by a combination of the same or different kinds of twoor more processors (for example, a combination of a plurality of FPGAs,a combination of a CPU and an FPGA, or a combination of a CPU and aGPU). In addition, a plurality of processing units may be configured byone processor. As an example of configuring a plurality of processingunits by one processor, first, as represented by a computer, such as aclient or a server, there is a form in which one processor is configuredby a combination of one or more CPUs and software and this processorfunctions as a plurality of processing units. Second, as represented bya system on chip (SoC) or the like, there is a form of using a processorfor realizing the function of the entire system including a plurality ofprocessing units with one integrated circuit (IC) chip. Thus, variousprocessing units are configured by using one or more of theabove-described various processors as hardware structures.

More specifically, the hardware structure of these various processors isan electrical circuit (circuitry) in the form of a combination ofcircuit elements, such as semiconductor elements. The hardware structureof the storage unit is a storage device such as a hard disc drive (HDD)or a solid state drive (SSD).

The present invention can also be implemented by the followingalternative embodiment.

A processor device is provided. The processor device is configured to:acquire a medical image obtained by imaging an observation target;calculate a feature amount of the observation target for each of pixelsof an image region of the medical image or for each of divided regionsobtained by dividing the image region of the medical image into aspecific size; and determine a disease stage of the observation targetby using a spatial distribution of the feature amount.

EXPLANATION OF REFERENCES

-   10: image processing system-   11: medical image acquisition unit-   12: medical image analysis processing unit-   13: display unit-   15: display control unit-   16: input receiving unit-   17: overall control unit-   18: saving unit-   21: endoscope system-   22: PACS-   31: endoscope-   32: light source device-   33: processor device-   34: monitor-   35: medical image acquisition unit-   36: medical image analysis processing unit-   37: display control unit-   40: effective region setting unit-   42: feature amount calculation unit-   43: second region integration unit-   44: stage determination unit-   44 a: distribution index value calculation unit-   44 b: determination unit-   44 c: first region integration unit-   46: dark area-   48: edge-   50: specular reflection-   610: diagnostic support apparatus-   621: first examination apparatus-   622: second examination apparatus-   623: N-th examination apparatus-   626: network-   630: medical service support apparatus-   RP: image region-   Rx: effective region-   P: divided region-   P1, P2, Pn, Ps, Pt, Pu, Pv, Pw: divided region-   Pa, Pb, Pc, Pd, Pf: divided region (abnormal region)-   IR1, IR2, IR3: integrated abnormal region-   IRx: integrated region

What is claimed is:
 1. A medical image processing system comprising: aprocessor configured to function as: a medical image acquisition unitthat acquires a medical image obtained by imaging an observation target;a feature amount calculation unit that calculates a feature amount ofthe observation target for each of pixels of an image region of themedical image or for each of divided regions obtained by dividing theimage region of the medical image into a specific size; and a stagedetermination unit that determines a disease stage of the observationtarget by using a spatial distribution of the feature amount, whereinthe stage determination unit determines between an early stage and aprogress stage, and a variation in the spatial distribution of thefeature amount in the progress stage is larger than a variation in thespatial distribution of the feature amount in the early stage.
 2. Themedical image processing system according to claim 1, wherein the stagedetermination unit includes a distribution index value calculation unitthat calculates a distribution index value which is an index value ofthe spatial distribution of the feature amount, and a determination unitthat determines the disease stage based on the distribution index value.3. The medical image processing system according to claim 2, wherein thedistribution index value is a variance of the feature amount, and adegree of progress of the disease stage increases as the variance of thefeature amount increases.
 4. The medical image processing systemaccording to claim 2, wherein the distribution index value is an indexvalue related to a spatial distribution of an abnormal region indicatinga pixel or a divided region where the feature amount is outside aspecific range.
 5. The medical image processing system according toclaim 4, wherein the index value related to the spatial distribution ofthe abnormal region is at least one of the number or an area of theabnormal regions or a ratio occupied by the abnormal region in the imageregion, and a degree of progress of the disease stage increases as thenumber or the area of the abnormal regions or the ratio of the abnormalregion increases.
 6. The medical image processing system according toclaim 2, wherein the stage determination unit includes a first regionintegration unit that integrates abnormal regions indicating a pixel ora divided region where the feature amount is outside a specific range,and the distribution index value is an index value related to a spatialdistribution of a specific integrated abnormal region that satisfies aspecific condition among integrated abnormal regions in which theabnormal regions are integrated by the first region integration unit. 7.The medical image processing system according to claim 2, wherein theprocessor further configured to function as: a second region integrationunit that integrates adjacent pixels or divided regions in a case wherea feature amount of the adjacent pixels or divided regions is within afeature amount range, wherein the distribution index value is an indexvalue related to a spatial distribution of an integrated region in whichthe adjacent pixels or divided regions are integrated by the secondregion integration unit.
 8. The medical image processing systemaccording to claim 1, wherein the feature amount is at least one of ablood vessel density, a blood vessel contrast, change in a blood vesselwidth, or an average value of the blood vessel width.
 9. The medicalimage processing system according to claim 1, wherein the feature amountcalculation unit calculates different types of a plurality ofcalculation feature amounts for each pixel or for each divided region,and calculates a first calculation value obtained by calculation basedon the plurality of calculation feature amounts, as the feature amount.10. The medical image processing system according to claim 9, whereinthe calculation feature amount is a blood vessel density of a firstlayer blood vessel and a blood vessel density of a second layer bloodvessel different from the first layer blood vessel, and the firstcalculation value is a ratio of the blood vessel density of the firstlayer blood vessel to the blood vessel density of the second layer bloodvessel.
 11. The medical image processing system according to claim 1,wherein the feature amount calculation unit calculates different typesof a plurality of calculation feature amounts for each pixel or for eachdivided region, and the stage determination unit includes a distributionindex value calculation unit that calculates a calculation distributionindex value which is an index value of a spatial distribution of each ofthe calculation feature amounts, and calculates a second calculationvalue obtained by calculation based on the calculation distributionindex value, as a distribution index value, and a determination unitthat determines the disease stage based on the distribution index value.12. The medical image processing system according to claim 1, whereinthe processor further configured to function as: an effective regionsetting unit that sets an effective region in which the disease stage isdeterminable in the image region, wherein the feature amount calculationunit calculates the feature amount for each divided region in theeffective region.
 13. The medical image processing system according toclaim 1, wherein, in a case of calculating the feature amount for eachpixel, the feature amount calculation unit calculates a feature amountof a specific region, which is obtained from the specific regionincluding the pixel, as the feature amount for each pixel.
 14. A medicalimage processing system comprising: a processor configured to functionas: a medical image acquisition unit that acquires a medical imageobtained by imaging an observation target; a feature amount calculationunit that calculates a feature amount of the observation target for eachof pixels of an image region of the medical image or for each of dividedregions obtained by dividing the image region of the medical image intoa specific size; and a stage determination unit that determines adisease stage of the observation target by using a spatial distributionof the feature amount, wherein the stage determination unit includes: adistribution index value calculation unit that calculates a distributionindex value which is an index value of the spatial distribution of thefeature amount, a determination unit that determines the disease stagebased on the distribution index value, and a first region integrationunit that integrates abnormal regions indicating a pixel or a dividedregion where the feature amount is outside a specific range, amongintegrated abnormal regions in which the abnormal regions are integratedby the first region integration unit, the integrated abnormal regionwhose area is equal to or larger than a certain value is set as aspecific integrated abnormal region, and the integrated abnormal regionwhose area is less than the certain value is not set as the specificintegrated abnormal region, and the distribution index value is an indexvalue related to a spatial distribution of the specific integratedabnormal region.